Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:31:37.328021
Analysis finished2020-08-25 00:31:58.468505
Duration21.14 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz5 is highly correlated with oz3High correlation
oz3 is highly correlated with oz5High correlation
oz1 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.096632331609726e-09
Minimum-2.500635862350464
Maximum2.5474050045013428
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:31:58.693604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.500635862
5-th percentile-1.620128697
Q1-0.7585654706
median-0.03730001114
Q30.7662046701
95-th percentile1.643360603
Maximum2.547405005
Range5.048040867
Interquartile range (IQR)1.524770141

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)911882652.9
Kurtosis-0.7330003966
Mean1.096632332e-09
Median Absolute Deviation (MAD)0.7608862426
Skewness0.02003471161
Sum1.096632332e-06
Variance0.9999999997
2020-08-25T00:31:58.801822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.685544550410.1%
 
-0.277670681510.1%
 
0.903015971210.1%
 
-0.437835067510.1%
 
0.132979780410.1%
 
-0.070884235210.1%
 
-0.680354416410.1%
 
0.0166417844610.1%
 
1.63538825510.1%
 
-1.20445549510.1%
 
0.531911909610.1%
 
-0.304041802910.1%
 
0.793097555610.1%
 
-0.327476173610.1%
 
1.3255274310.1%
 
1.37630355410.1%
 
1.39968991310.1%
 
-0.809899091710.1%
 
-0.815099954610.1%
 
-0.303282380110.1%
 
-0.0791820362210.1%
 
-0.387039959410.1%
 
0.699859380710.1%
 
-1.78252863910.1%
 
0.906887829310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.50063586210.1%
 
-2.21889901210.1%
 
-2.20753431310.1%
 
-2.14822006210.1%
 
-2.1395771510.1%
 
-2.13540601710.1%
 
-2.10089874310.1%
 
-2.07579898810.1%
 
-2.0530095110.1%
 
-2.02881264710.1%
 
ValueCountFrequency (%) 
2.54740500510.1%
 
2.19020652810.1%
 
2.12649726910.1%
 
2.10744953210.1%
 
2.10450577710.1%
 
2.07299017910.1%
 
2.06076240510.1%
 
2.05661606810.1%
 
2.05526351910.1%
 
2.03963327410.1%
 

oz2
Real number (ℝ)

Distinct count999
Unique (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.975598469376564e-10
Minimum-1.733625054359436
Maximum1.7340246438980105
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:31:58.912878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.733625054
5-th percentile-1.550500757
Q1-0.8690677732
median0.02359887213
Q30.875529632
95-th percentile1.57762332
Maximum1.734024644
Range3.467649698
Interquartile range (IQR)1.744597405

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)1673472550
Kurtosis-1.161209912
Mean5.975598469e-10
Median Absolute Deviation (MAD)0.8745984137
Skewness0.01878079951
Sum5.975598469e-07
Variance1.000000002
2020-08-25T00:31:59.016813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.26891660720.2%
 
1.66796684310.1%
 
1.51687312110.1%
 
-0.738927066310.1%
 
1.57540726710.1%
 
-1.25128889110.1%
 
1.45440912210.1%
 
-0.930328428710.1%
 
-0.0044910698210.1%
 
1.42705583610.1%
 
1.63017749810.1%
 
-1.46611022910.1%
 
-0.627584695810.1%
 
0.269217014310.1%
 
-1.53640961610.1%
 
-1.03547084310.1%
 
1.68489396610.1%
 
-0.251288235210.1%
 
1.62948238810.1%
 
1.16977691710.1%
 
1.40545105910.1%
 
-0.766239643110.1%
 
0.970090627710.1%
 
1.1535679110.1%
 
0.568968415310.1%
 
Other values (974)97497.4%
 
ValueCountFrequency (%) 
-1.73362505410.1%
 
-1.73189866510.1%
 
-1.72542369410.1%
 
-1.72489416610.1%
 
-1.72329807310.1%
 
-1.72179174410.1%
 
-1.71790027610.1%
 
-1.71733963510.1%
 
-1.7162729510.1%
 
-1.71121382710.1%
 
ValueCountFrequency (%) 
1.73402464410.1%
 
1.73339283510.1%
 
1.73200583510.1%
 
1.72973954710.1%
 
1.72963690810.1%
 
1.72689282910.1%
 
1.7236392510.1%
 
1.72099685710.1%
 
1.71822154510.1%
 
1.71551811710.1%
 

oz3
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.407514524577664e-10
Minimum-2.0301146507263184
Maximum3.437894344329834
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:31:59.135241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.030114651
5-th percentile-1.295166916
Q1-0.7581044137
median-0.2042397931
Q30.6235760152
95-th percentile1.942621613
Maximum3.437894344
Range5.468008995
Interquartile range (IQR)1.381680429

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1189412160
Kurtosis0.1491315611
Mean-8.407514525e-10
Median Absolute Deviation (MAD)0.6410218701
Skewness0.7724743122
Sum-8.407514525e-07
Variance1.000000001
2020-08-25T00:31:59.241525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.607386767910.1%
 
-0.208654910310.1%
 
-1.38805961610.1%
 
-0.672543823710.1%
 
-1.06774294410.1%
 
-1.0794532310.1%
 
1.23202884210.1%
 
1.27084159910.1%
 
-0.127116605610.1%
 
0.0764976218310.1%
 
0.660806417510.1%
 
1.39784085810.1%
 
-0.00275671551910.1%
 
0.41535875210.1%
 
-0.768216311910.1%
 
0.332348585110.1%
 
1.49878287310.1%
 
-0.47305622710.1%
 
-0.90171176210.1%
 
-0.930318117110.1%
 
-1.0637574210.1%
 
-1.49089002610.1%
 
-0.572887599510.1%
 
-0.349920183410.1%
 
-0.0628212094310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.03011465110.1%
 
-1.84317815310.1%
 
-1.83492386310.1%
 
-1.80960154510.1%
 
-1.74283897910.1%
 
-1.67479753510.1%
 
-1.64418053610.1%
 
-1.61129140910.1%
 
-1.58372831310.1%
 
-1.58233225310.1%
 
ValueCountFrequency (%) 
3.43789434410.1%
 
3.17844033210.1%
 
3.12226080910.1%
 
3.09861898410.1%
 
3.06726622610.1%
 
3.03837990810.1%
 
2.94549989710.1%
 
2.93123483710.1%
 
2.92351436610.1%
 
2.9216337210.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.977934390306473e-10
Minimum-1.7857121229171753
Maximum1.6677552461624146
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:31:59.350065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.785712123
5-th percentile-1.577593577
Q1-0.8501406759
median-0.00562096457
Q30.8595988899
95-th percentile1.536319309
Maximum1.667755246
Range3.453467369
Interquartile range (IQR)1.709739566

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1433088855
Kurtosis-1.190053311
Mean-6.97793439e-10
Median Absolute Deviation (MAD)0.8581504307
Skewness-0.04155257123
Sum-6.97793439e-07
Variance1.000000001
2020-08-25T00:31:59.454206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.526219129610.1%
 
-0.678204178810.1%
 
-0.717473626110.1%
 
-1.41969633110.1%
 
-1.40441703810.1%
 
-1.29554545910.1%
 
-1.41539168410.1%
 
1.24741864210.1%
 
1.54819464710.1%
 
-1.55599987510.1%
 
0.629562020310.1%
 
1.64584016810.1%
 
1.29189264810.1%
 
1.29469859610.1%
 
1.53254866610.1%
 
1.26243567510.1%
 
-0.918584823610.1%
 
1.03413164610.1%
 
-0.914706349410.1%
 
-1.19268834610.1%
 
-0.060384858410.1%
 
1.3020552410.1%
 
-1.49345815210.1%
 
-0.344066917910.1%
 
0.230137854810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.78571212310.1%
 
-1.78392338810.1%
 
-1.7782784710.1%
 
-1.77597510810.1%
 
-1.7738536610.1%
 
-1.75037765510.1%
 
-1.74610745910.1%
 
-1.74518692510.1%
 
-1.74067151510.1%
 
-1.7286038410.1%
 
ValueCountFrequency (%) 
1.66775524610.1%
 
1.66306054610.1%
 
1.66197478810.1%
 
1.66114211110.1%
 
1.65766692210.1%
 
1.65683484110.1%
 
1.65274870410.1%
 
1.64940881710.1%
 
1.64584016810.1%
 
1.64492988610.1%
 

oz5
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7577840480953455e-09
Minimum-1.6995395421981812
Maximum4.336246967315674
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:31:59.572211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.699539542
5-th percentile-1.043223542
Q1-0.7137553245
median-0.3258197904
Q30.442876406
95-th percentile2.182011878
Maximum4.336246967
Range6.03578651
Interquartile range (IQR)1.15663173

Descriptive statistics

Standard deviation0.9999999965
Coefficient of variation (CV)-568898095.1
Kurtosis1.925856816
Mean-1.757784048e-09
Median Absolute Deviation (MAD)0.4802921712
Skewness1.416881315
Sum-1.757784048e-06
Variance0.9999999929
2020-08-25T00:31:59.688241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.982423663110.1%
 
-0.030314166110.1%
 
-1.04228210410.1%
 
-1.01696014410.1%
 
-0.815119266510.1%
 
-1.17320239510.1%
 
-0.768239140510.1%
 
0.0074209268210.1%
 
2.59638452510.1%
 
-0.234051153110.1%
 
-0.293296933210.1%
 
1.04812419410.1%
 
-0.342123508510.1%
 
-1.01302182710.1%
 
3.14322757710.1%
 
-0.807285964510.1%
 
-0.551400244210.1%
 
-0.590487897410.1%
 
0.142250567710.1%
 
-1.05206334610.1%
 
-1.54425120410.1%
 
-0.590483665510.1%
 
-0.342114508210.1%
 
-0.512352824210.1%
 
0.752585709110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.69953954210.1%
 
-1.54425120410.1%
 
-1.43903112410.1%
 
-1.41677594210.1%
 
-1.30404508110.1%
 
-1.29195296810.1%
 
-1.29151785410.1%
 
-1.25562965910.1%
 
-1.23373711110.1%
 
-1.2271338710.1%
 
ValueCountFrequency (%) 
4.33624696710.1%
 
4.06812715510.1%
 
4.05235385910.1%
 
4.00825214410.1%
 
3.96896600710.1%
 
3.82833981510.1%
 
3.39873266210.1%
 
3.38259887710.1%
 
3.19574236910.1%
 
3.14322757710.1%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2212549327372812e-09
Minimum-1.751087307929993
Maximum1.7382028102874756
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:31:59.805280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.751087308
5-th percentile-1.552269131
Q1-0.8957294077
median0.008427586872
Q30.8316136748
95-th percentile1.57166937
Maximum1.73820281
Range3.489290118
Interquartile range (IQR)1.727343082

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-818829855.8
Kurtosis-1.18171992
Mean-1.221254933e-09
Median Absolute Deviation (MAD)0.8526430461
Skewness-0.009465278572
Sum-1.221254933e-06
Variance1.000000001
2020-08-25T00:31:59.908362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.451963782310.1%
 
1.08619570710.1%
 
-0.427303463210.1%
 
0.197434902210.1%
 
1.15369248410.1%
 
-1.1383484610.1%
 
0.750515282210.1%
 
-1.41272330310.1%
 
1.02601325510.1%
 
-1.09507644210.1%
 
-1.71616852310.1%
 
-0.305018067410.1%
 
-1.34697282310.1%
 
-0.200420081610.1%
 
0.397131472810.1%
 
-1.49478554710.1%
 
0.570933222810.1%
 
-0.630321085510.1%
 
0.409503489710.1%
 
0.142739564210.1%
 
-0.717831373210.1%
 
-0.403058946110.1%
 
-0.149085581310.1%
 
-0.041421763610.1%
 
-0.89321404710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.75108730810.1%
 
-1.7497843510.1%
 
-1.74640297910.1%
 
-1.74378204310.1%
 
-1.72993302310.1%
 
-1.72415494910.1%
 
-1.72001874410.1%
 
-1.71838939210.1%
 
-1.7176660310.1%
 
-1.71616852310.1%
 
ValueCountFrequency (%) 
1.7382028110.1%
 
1.73269689110.1%
 
1.73218798610.1%
 
1.72902810610.1%
 
1.72683155510.1%
 
1.72667825210.1%
 
1.72436225410.1%
 
1.72216939910.1%
 
1.72203397810.1%
 
1.7176419510.1%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.519265636801719e-10
Minimum-1.7512495517730713
Maximum1.6997910737991333
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:00.027836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.751249552
5-th percentile-1.561610013
Q1-0.9080786109
median-0.00643440173
Q30.862203896
95-th percentile1.545328164
Maximum1.699791074
Range3.451040626
Interquartile range (IQR)1.770282507

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1329917108
Kurtosis-1.211351847
Mean-7.519265637e-10
Median Absolute Deviation (MAD)0.8991408141
Skewness-0.005955620208
Sum-7.519265637e-07
Variance1.000000002
2020-08-25T00:32:00.130601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.936384439510.1%
 
1.4661749610.1%
 
1.49747455110.1%
 
-1.24811863910.1%
 
-1.31174147110.1%
 
1.17715585210.1%
 
-0.434913307410.1%
 
0.173510834610.1%
 
-0.865916550210.1%
 
-1.32558071610.1%
 
-0.545389473410.1%
 
1.21618473510.1%
 
1.14587163910.1%
 
0.640527844410.1%
 
0.787776529810.1%
 
-0.389001399310.1%
 
0.18983139110.1%
 
0.341147899610.1%
 
-1.65365254910.1%
 
-1.7434946310.1%
 
-1.31770622710.1%
 
-1.33332490910.1%
 
-0.672516703610.1%
 
-1.0833127510.1%
 
-1.3518127210.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.75124955210.1%
 
-1.74405157610.1%
 
-1.7434946310.1%
 
-1.7434327610.1%
 
-1.73825073210.1%
 
-1.73805737510.1%
 
-1.73063290110.1%
 
-1.73037493210.1%
 
-1.73002016510.1%
 
-1.72973322910.1%
 
ValueCountFrequency (%) 
1.69979107410.1%
 
1.69836723810.1%
 
1.69766271110.1%
 
1.69124484110.1%
 
1.68607926410.1%
 
1.68396377610.1%
 
1.67837023710.1%
 
1.67528665110.1%
 
1.66985952910.1%
 
1.66675591510.1%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.4620891306549311e-09
Minimum-1.7173829078674316
Maximum1.7564132213592532
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:00.248037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.717382908
5-th percentile-1.520044726
Q1-0.8734428883
median-0.03988509439
Q30.8703573793
95-th percentile1.591778094
Maximum1.756413221
Range3.473796129
Interquartile range (IQR)1.743800268

Descriptive statistics

Standard deviation0.9999999995
Coefficient of variation (CV)-683952830.6
Kurtosis-1.175279662
Mean-1.462089131e-09
Median Absolute Deviation (MAD)0.8669746909
Skewness0.0708496673
Sum-1.462089131e-06
Variance0.9999999991
2020-08-25T00:32:00.349591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.593745410410.1%
 
0.418294787410.1%
 
-0.906351864310.1%
 
1.14754152310.1%
 
-1.26955294610.1%
 
-0.122901968710.1%
 
0.897149920510.1%
 
-1.36070108410.1%
 
-1.15492534610.1%
 
1.34507048110.1%
 
-0.443689286710.1%
 
-1.04555714110.1%
 
0.733079433410.1%
 
0.375328540810.1%
 
0.940108597310.1%
 
0.36555835610.1%
 
0.57093077910.1%
 
0.904943227810.1%
 
-1.35284674210.1%
 
1.50748455510.1%
 
-1.15752375110.1%
 
1.71188914810.1%
 
-0.105422303110.1%
 
-0.487539887410.1%
 
1.48954629910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.71738290810.1%
 
-1.71667492410.1%
 
-1.71605730110.1%
 
-1.71157705810.1%
 
-1.71153950710.1%
 
-1.70694875710.1%
 
-1.70426058810.1%
 
-1.70153868210.1%
 
-1.69889211710.1%
 
-1.69415426310.1%
 
ValueCountFrequency (%) 
1.75641322110.1%
 
1.75428497810.1%
 
1.75031566610.1%
 
1.74810314210.1%
 
1.74795746810.1%
 
1.74609601510.1%
 
1.74511730710.1%
 
1.74039900310.1%
 
1.73808884610.1%
 
1.73644685710.1%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.451737135648727e-10
Minimum-1.7099616527557373
Maximum1.7634416818618774
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:00.463046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.709961653
5-th percentile-1.562531668
Q1-0.8387745172
median-0.04134033434
Q30.8867355585
95-th percentile1.566410875
Maximum1.763441682
Range3.473403335
Interquartile range (IQR)1.725510076

Descriptive statistics

Standard deviation0.9999999987
Coefficient of variation (CV)-1549970152
Kurtosis-1.193187975
Mean-6.451737136e-10
Median Absolute Deviation (MAD)0.8667571843
Skewness0.0238112925
Sum-6.451737136e-07
Variance0.9999999973
2020-08-25T00:32:00.566557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.29109740310.1%
 
-0.171555787310.1%
 
-0.34605258710.1%
 
1.35685896910.1%
 
1.74747943910.1%
 
1.34513235110.1%
 
-0.651080250710.1%
 
0.235357254710.1%
 
0.119959101110.1%
 
0.815140366610.1%
 
-1.12097120310.1%
 
-0.652475416710.1%
 
1.71141588710.1%
 
-0.983102202410.1%
 
1.14588868610.1%
 
-0.972523868110.1%
 
0.529950916810.1%
 
0.880352795110.1%
 
1.34900188410.1%
 
-0.499359160710.1%
 
-1.37999093510.1%
 
-0.924498438810.1%
 
1.60289955110.1%
 
-0.779963433710.1%
 
0.906084716310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.70996165310.1%
 
-1.68837654610.1%
 
-1.68335759610.1%
 
-1.68335473510.1%
 
-1.6801322710.1%
 
-1.6789668810.1%
 
-1.67864167710.1%
 
-1.67489457110.1%
 
-1.67085063510.1%
 
-1.66727447510.1%
 
ValueCountFrequency (%) 
1.76344168210.1%
 
1.76064586610.1%
 
1.75878465210.1%
 
1.75850772910.1%
 
1.74958038310.1%
 
1.74747943910.1%
 
1.74474215510.1%
 
1.74376118210.1%
 
1.73620128610.1%
 
1.73547458610.1%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.122274160385132e-11
Minimum-1.6871379613876345
Maximum1.7293839454650881
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:00.684823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.687137961
5-th percentile-1.53542127
Q1-0.8784653544
median-0.004982982529
Q30.892669633
95-th percentile1.560977441
Maximum1.729383945
Range3.416521907
Interquartile range (IQR)1.771134987

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)1.952257862e+10
Kurtosis-1.256889818
Mean5.12227416e-11
Median Absolute Deviation (MAD)0.8875654936
Skewness0.02412496272
Sum5.12227416e-08
Variance1
2020-08-25T00:32:00.788773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.97460877910.1%
 
-1.1888418210.1%
 
0.0288302637610.1%
 
0.00273658009210.1%
 
0.682330906410.1%
 
0.469094663910.1%
 
1.30215692510.1%
 
1.26699757610.1%
 
0.601323664210.1%
 
-0.93603414310.1%
 
-0.512396156810.1%
 
1.56895875910.1%
 
-1.61189222310.1%
 
0.742800116510.1%
 
-0.484038978810.1%
 
1.23962020910.1%
 
1.0911372910.1%
 
-1.66539752510.1%
 
-1.23570525610.1%
 
-0.207685127910.1%
 
1.24867725410.1%
 
-1.12631881210.1%
 
-1.3319543610.1%
 
-0.69763213410.1%
 
0.0058822152210.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.68713796110.1%
 
-1.68254910.1%
 
-1.6819986110.1%
 
-1.67967402910.1%
 
-1.67387020610.1%
 
-1.66706395110.1%
 
-1.66666126310.1%
 
-1.66539752510.1%
 
-1.66312015110.1%
 
-1.65895891210.1%
 
ValueCountFrequency (%) 
1.72938394510.1%
 
1.72443163410.1%
 
1.72254884210.1%
 
1.72154569610.1%
 
1.71809554110.1%
 
1.7164132610.1%
 
1.71496939710.1%
 
1.71492779310.1%
 
1.71108090910.1%
 
1.70879292510.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2833752416341326e-09
Minimum-2.5857164859771733
Maximum3.925671815872192
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:00.904172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.585716486
5-th percentile-1.889301348
Q1-0.6283558011
median0.1291956306
Q30.669326216
95-th percentile1.379305595
Maximum3.925671816
Range6.511388302
Interquartile range (IQR)1.297682017

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-779195335.6
Kurtosis0.3015547828
Mean-1.283375242e-09
Median Absolute Deviation (MAD)0.6352087557
Skewness-0.1652261506
Sum-1.283375242e-06
Variance1.000000004
2020-08-25T00:32:01.009193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.17594218310.1%
 
0.541678845910.1%
 
-0.608103334910.1%
 
0.320652157110.1%
 
2.1511511810.1%
 
-0.635441958910.1%
 
-0.184250533610.1%
 
1.36761212310.1%
 
0.459625154710.1%
 
-0.594420731110.1%
 
-0.676980376210.1%
 
0.969418108510.1%
 
0.323575645710.1%
 
-1.08276677110.1%
 
0.65105593210.1%
 
0.218878611910.1%
 
0.35578957210.1%
 
-0.424707114710.1%
 
0.701829791110.1%
 
-0.502610027810.1%
 
0.0843306481810.1%
 
-0.69401127110.1%
 
-1.39664566510.1%
 
0.99088400610.1%
 
0.509088039410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.58571648610.1%
 
-2.53108024610.1%
 
-2.45810198810.1%
 
-2.39804792410.1%
 
-2.37294530910.1%
 
-2.32430338910.1%
 
-2.3078792110.1%
 
-2.28635835610.1%
 
-2.2743465910.1%
 
-2.25181388910.1%
 
ValueCountFrequency (%) 
3.92567181610.1%
 
3.67546629910.1%
 
3.55743098310.1%
 
3.37212467210.1%
 
3.04222774510.1%
 
2.89656591410.1%
 
2.45269727710.1%
 
2.38887810710.1%
 
2.35528802910.1%
 
2.23476028410.1%
 

Interactions

2020-08-25T00:31:37.827399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:37.967784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:38.116002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:38.261242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:38.414594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:38.562381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:38.711922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:38.864410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:39.178904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:39.331986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:39.481208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:39.629248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:39.781431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:39.944613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:40.100342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:40.270154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:40.433484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:40.596304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:40.765462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:40.929384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:41.090608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:41.253792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:41.419379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:41.565281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:41.720405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:41.870848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:42.026384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:42.179336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:42.335413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:42.493989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:42.650499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:42.805305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:42.962800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:43.117356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:43.269537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:43.431345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:43.586959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:43.747653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:44.087896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:44.248886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:44.411608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:44.573871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:44.735549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:44.897103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:45.057290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:45.208565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:45.367430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:45.522965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:45.682765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:45.842239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:46.003759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:46.164980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:46.325968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:46.490680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:46.651095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:46.809438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:46.963111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:47.127883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:47.284484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:47.447379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:47.615891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:47.777743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:47.938347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:48.111391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:48.282991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:48.444796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:48.606901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:48.923823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:49.087015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:49.241994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:49.402479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:49.567122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:49.731460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:49.895352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:50.059724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:50.230956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:50.394729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:50.563540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:50.716922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:50.884894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:51.055711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:51.219750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:51.380645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:51.544806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:51.707055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:51.868620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:52.032722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:52.194107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:52.355090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:52.508196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:52.683515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:52.845684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:53.006025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:53.168110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:53.328276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:53.489372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:53.814518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:53.974322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:54.139456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:54.305014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:54.457690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:54.621942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:54.779674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:54.942346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:55.104792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:55.267304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:55.430409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:55.594873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:55.769322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:55.939109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:56.109293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:56.264544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:56.428974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:56.586525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:56.779539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:56.989614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:57.156169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:57.320653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:57.482517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:57.643791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:57.814404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:32:01.137247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:32:01.354482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:32:01.734107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:32:01.964974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:31:58.089630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:31:58.363924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
00.688222-0.049180-0.000855-0.672358-0.060101-1.1013620.2668300.473687-1.6387700.607168-0.670287
1-0.048359-0.804375-0.4359081.494861-0.4385101.2903000.755282-1.127090-1.2231261.2485281.272470
21.6128051.1870921.0672660.2692281.3206611.1857871.4892590.022325-0.385915-0.5507160.605527
30.0933870.756280-0.273233-1.719814-0.417640-0.399598-0.686605-0.890769-1.373113-0.673659-1.648091
4-0.467931-0.826143-0.204402-0.039011-0.243801-0.722189-0.0211260.6732241.609104-0.8675020.517880
5-0.620478-1.388932-0.9930940.910059-1.188882-0.7049040.834794-0.208343-1.2858911.0631310.293131
61.6769141.4038671.8951660.8073192.553450-1.1854640.351976-0.2732920.3959510.4201722.173286
70.8316770.4119410.220168-1.3110770.285163-0.645828-0.138015-1.168768-1.0110280.343331-1.752741
8-0.4905890.3529840.145466-0.063797-0.424981-0.547393-0.580457-0.909662-1.6434820.0238760.273076
9-1.086659-1.610372-0.915049-1.720984-0.759307-1.678211-0.2633130.649684-1.3052660.353345-0.647929

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
9900.4962421.3179802.2957411.4523991.682932-1.447959-0.7240011.5337900.813180-1.278855-0.433304
991-0.939040-0.2865410.7731571.604770-0.1247971.3284311.181271-0.801707-0.5054561.3948021.403504
992-0.991449-1.076754-0.4852401.440239-0.6713561.039715-1.6025571.6268421.6721701.1579260.651056
993-0.338998-0.775177-0.918950-0.342896-0.629281-0.6195100.676036-1.0140740.7674110.2561990.507428
994-0.621517-1.235335-0.780156-1.572292-0.924072-0.9294890.630376-0.3368700.262345-0.218666-0.706129
995-1.076263-1.152052-1.029224-1.403857-0.769279-0.146821-1.159414-0.8051021.453137-0.005665-0.371922
996-0.762108-1.592824-1.127128-0.178239-0.5156741.098000-1.413517-0.217172-1.134241-1.4573060.021494
997-0.798345-0.6173780.5302661.073621-0.191371-0.8249630.8146801.087503-0.503277-1.4842341.125683
9980.4939851.2570400.532908-0.8455170.641986-0.4781110.205339-0.382842-1.2658221.666419-2.086017
999-1.543365-1.711140-1.5444850.555247-1.181932-1.077977-0.450332-0.2640180.996544-0.3456400.433111